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Epidemics. Author manuscript; available in PMC 2010 September 1.

Published in final edited form as:

PMCID: PMC2796779

NIHMSID: NIHMS150315

Influenza infections often predispose individuals to consecutive bacterial infections. Both during seasonal and pandemic influenza outbreaks, morbidity and mortality due to secondary bacterial infections can be substantial. With the help of a mathematical model, we investigate the potential impact of such bacterial infections during an influenza pandemic, and we analyze how antiviral and antibacterial treatment or prophylaxis affect morbidity and mortality. We consider different scenarios for the spread of bacteria, the emergence of antiviral resistance, and different levels of severity for influenza infections (1918-like and 2009-like). We find that while antibacterial intervention strategies are unlikely to play an important role in reducing the overall number of cases, such interventions can lead to a significant reduction in mortality and in the number of bacterial infections. Antibacterial interventions become even more important if one considers the – very likely – scenario that during a pandemic outbreak, influenza strains resistant to antivirals emerge. Overall, our study suggests that pandemic preparedness plans should consider intervention strategies based on antibacterial treatment or prophylaxis through drugs or vaccines as part of the overall control strategy. A major caveat for our results is the lack of data that would allow precise estimation of many of the model parameters. As our results show, this leads to very large uncertainty in model outcomes. As we discuss, precise assessment of the impact of antibacterial strategies during an influenza pandemic will require the collection of further data to better estimate key parameters, especially those related to the bacterial infections and the impact of antibacterial intervention strategies.

Infections with both seasonal and pandemic strains of influenza A virus can render hosts more susceptible to secondary bacterial infections, often resulting in significant morbidity and mortality (6, 8, 11, 12, 26, 27, 31, 64, 66, 73, 74, 77, 85). The bacteria most common found in secondary infections are Streptococcus pneumoniae, Staphylococcus aureus, Haemophilus influenzae and Neisseria meningitides, with others playing minor roles (12, 15, 28, 31, 38, 43, 44, 53, 60, 64, 66, 74, 81, 84, 90). Despite the general awareness of the importance of secondary bacterial infections, current studies that investigate containment or mitigation of a possible influenza pandemic do not consider such infections and the possible impact of antibacterial interventions (20, 21, 25, 29, 58, 67). Here, we model a pandemic influenza outbreak in the U.S. and explicitly consider secondary bacterial infections. We investigate how intervention strategies based on antiviral (AV) or antibacterial (AB) prophylaxis or treatment affect the number of influenza and bacteria cases and deaths. We study the impact of AV and AB control strategies in the context of both a severe and relatively mild pandemic, modeled after the 1918 and 2009 H1N1 outbreaks, respectively. For prophylaxis or treatment with antivirals, we consider the currently available neuraminidase inhibitors, i.e. oseltamivir and zanamivir (75). (For a recent study that considers administration of multiple antivirals during an influenza pandemic, see Wu et al (91)). For the antibacterial control strategies, we focus on prophylaxis in the form of vaccination against Streptococcus pneumonia (51, 61, 71), prophylactic administration of broad-spectrum antibacterial drugs (e.g. flouroquinolones, oxazolidinones or similar (18, 96)), or a mixture of the two. Antibacterial treatment is assumed to occur with the same type of broad-spectrum drugs.

We find that while antibacterial intervention strategies are unlikely to play an important role in reducing the overall number of cases, such interventions can lead to a significant reduction in mortality and in the number of bacterial infections. We show how antibacterial interventions become even more important if one considers the – very likely – scenario that during a pandemic outbreak, influenza strains resistant to antivirals emerge. The lack of precise estimation of many of the model parameters leads to rather large uncertainty in model outcomes. By performing a sensitivity analysis, we determine the parameters that have the most impact on the obtained results.

We use a compartmental, SIR-type model (3, 39) to study a pandemic outbreak in the U.S. Intervention strategies involve administration of antiviral (AV) drugs and antibacterial (AB) drugs or vaccines, either as prophylaxis or as treatment.

We assume that for a novel, pandemic strain, no immunity exists, the whole population is susceptible. A fraction *f _{p}* of the susceptibles,

Individuals who become infected with influenza are either untreated (u) or treated (t) with AV and prophylaxed (p) or not (n) with AB. We therefore have 4 compartments, labeled *I _{u}*

Additionally, it is possible that both influenza infected or bacteria infected hosts shed bacteria and that bacteria infected hosts still harbor some virus and spread the virus to susceptible hosts. We include transmission terms for all these possibilities in the force of infection terms (the *λ*’s in the equations below). The indexes indicate the status of the infected host and the pathogen that is spread, e.g. “bi” stands for a bacteria infected host spreading influenza, “ib” stands for an influenza infected host spreading bacteria, etc.

Hosts infected with bacteria (and possibly also still virus) can receive either AV or AB treatment, neither, or both, giving four compartments which we label *B _{u}*

Parameters governing the transmission processes of bacteria or influenza from the different classes of infected hosts.

$$\begin{array}{l}{\lambda}_{ii}={\beta}_{u,n}{I}_{u,n}+{\beta}_{u,p}{I}_{u,p}+{\beta}_{t,n}{I}_{t,n}+{\beta}_{t,p}{I}_{t,p}\\ {\lambda}_{bi}={\alpha}_{u,u}{B}_{u,u}+{\alpha}_{u,t}{B}_{u,t}+{\alpha}_{t,u}{B}_{t,u}+{\alpha}_{t,t}{B}_{t,t}\\ {\lambda}_{1}={\lambda}_{ii}+{\lambda}_{bi}\\ {\lambda}_{bb}={\gamma}_{u,u}{B}_{u,u}+{\gamma}_{u,t}{B}_{u,t}+{\gamma}_{t,u}{B}_{t,u}+{\gamma}_{t,t}{B}_{t,t}\\ {\lambda}_{ib}={\kappa}_{u,n}{I}_{u,n}+{\kappa}_{u,p}{I}_{u,p}+{\kappa}_{t,n}{I}_{t,n}+{\kappa}_{t,p}{I}_{t,p}\\ {\lambda}_{2}={\lambda}_{bb}+{\lambda}_{ib}\\ \stackrel{.}{S}=-{\lambda}_{1}(1-{e}_{p}{f}_{p})S\\ {\stackrel{.}{I}}_{u,n}=(1-{g}_{p})(1-{f}_{t})(1-{f}_{p}){\lambda}_{1}S-{\nu}_{u,n}{I}_{u,n}-{k}_{u,n}{\lambda}_{2}{I}_{u,n}\\ {\stackrel{.}{I}}_{u,p}={g}_{p}(1-{f}_{t})(1-{f}_{p}){\lambda}_{1}S-{\nu}_{u,p}{I}_{u,p}-{k}_{u,p}{\lambda}_{2}{I}_{u,p}\\ {\stackrel{.}{I}}_{t,n}=(1-{g}_{p})({f}_{t}(1-{f}_{p})+{f}_{p}(1-{e}_{p})){\lambda}_{1}S-{\nu}_{t,n}{I}_{t,n}-{k}_{t,n}{\lambda}_{2}{I}_{t,n}\\ {\stackrel{.}{I}}_{t,p}={g}_{p}({f}_{t}(1-{f}_{p})+{f}_{p}(1-{e}_{p})){\lambda}_{1}S-{\nu}_{t,p}{I}_{t,p}-{k}_{t,p}{\lambda}_{2}{I}_{t,p}\\ {\stackrel{.}{B}}_{u,u}=(1-{f}_{t})(1-{g}_{t})({\nu}_{u,n}{c}_{u,n}+{k}_{u,n}{\lambda}_{2}){I}_{u,n}-{\delta}_{u,n}{B}_{u,u}\\ {\stackrel{.}{B}}_{u,t}=(1-{f}_{t}){g}_{t}({\nu}_{u,n}{c}_{u,n}+{k}_{u,n}{\lambda}_{2}){I}_{u,n}+(1-{f}_{t})({\nu}_{u,p}{c}_{u,p}+{k}_{u,p}{\lambda}_{2}){I}_{u,p}-{\delta}_{u,t}{B}_{u,t}\\ {\stackrel{.}{B}}_{t,u}={f}_{t}(1-{g}_{t})({\nu}_{u,n}{c}_{u,n}+{k}_{u,n}{\lambda}_{2}){I}_{u,n}+(1-{g}_{t})({\nu}_{t,n}{c}_{t,n}+{k}_{t,n}{\lambda}_{2}){I}_{t,n}-{\delta}_{t,u}{B}_{t,u}\\ {\stackrel{.}{B}}_{t,t}={f}_{t}{g}_{t}({\nu}_{u,n}{c}_{u,n}+{k}_{u,n}{\lambda}_{2}){I}_{u,n}+{f}_{t}({\nu}_{u,p}{c}_{u,p}+{k}_{u,p}{\lambda}_{2}){I}_{u,p}+{g}_{t}({\nu}_{t,n}{c}_{t,n}+{k}_{t,n}{\lambda}_{2}){I}_{t,n}\\ +({\nu}_{t,p}{c}_{t,p}+{k}_{t,p}{\lambda}_{2}){I}_{t,p}-{\delta}_{t,t}{B}_{t,t}\end{array}$$

We only consider a single pandemic outbreak in our study. Since bacteria generally have longer generation times and lower mutation rates compared to influenza virus, it is reasonable to assume that an AB that is effective against a particular bacteria strain at the beginning of the pandemic will be effective throughout the outbreak. We therefore only model the potential of resistance generation by the virus against the AV. Resistance arises during AV treatment. A small fraction, *μ*, of hosts infected with drug sensitive influenza who receive AV treatment cause secondary infections with the resistant strain (32, 34). We assume that this fraction is the same for AV treated hosts infected with influenza or bacteria (the latter can still harbor some virus, as described in the previous section). For a resistant strain, AV treatment or prophylaxis has no impact but AB prophylaxis or treatment can make a difference. Our model therefore needs to be extended by four classes, influenza infecteds with resistant virus who receive AB prophylaxis, *I _{r}*

Additional model parameters for the AV resistant influenza strain. For most of the parameters we assumed that the drug resistant strain differs little from the drug sensitive strain.

$$\begin{array}{l}{\lambda}_{ii}={\beta}_{u,n}{I}_{u,n}+{\beta}_{u,p}{I}_{u,p}+(1-\mu ){\beta}_{t,n}{I}_{t,n}+(1-\mu ){\beta}_{t,p}{I}_{t,p}\\ {\lambda}_{bi}={\alpha}_{u,u}{B}_{u,u}+{\alpha}_{u,t}{B}_{u,t}+(1-\mu ){\alpha}_{t,u}{B}_{t,u}+(1-\mu ){\alpha}_{t,t}{B}_{t,t}\\ {\lambda}_{1}={\lambda}_{ii}+{\lambda}_{bi}\\ {\lambda}_{bb}={\gamma}_{u,u}{B}_{u,u}+{\gamma}_{u,t}{B}_{u,t}+{\gamma}_{t,u}{B}_{t,u}+{\gamma}_{t,t}{B}_{t,t}\\ {\lambda}_{ib}={\kappa}_{u,n}{I}_{u,n}+{\kappa}_{u,p}{I}_{u,p}+{\kappa}_{t,n}{I}_{t,n}+{\kappa}_{t,p}{I}_{t,p}\\ {\lambda}_{2}={\lambda}_{bb}+{\lambda}_{ib}\\ {\lambda}_{r}=\mu ({\beta}_{t,n}{I}_{t,n}+{\beta}_{t,p}{I}_{t,p}+{\alpha}_{t,u}{B}_{t,u}+{\alpha}_{t,t}{B}_{t,t})+{\beta}_{r,n}{I}_{r,n}+{\beta}_{r,p}{I}_{r,p}+{\alpha}_{r,u}{B}_{r,u}+{\alpha}_{r,t}{B}_{r,t}\\ \stackrel{.}{S}=-{\lambda}_{1}(1-{e}_{p}{f}_{p})S-{\lambda}_{r}S\\ {\stackrel{.}{I}}_{u,n}=(1-{g}_{p})(1-{f}_{t})(1-{f}_{p}){\lambda}_{1}S-{\nu}_{u,n}{I}_{u,n}-{k}_{u,n}{\lambda}_{2}{I}_{u,n}\\ {\stackrel{.}{I}}_{u,p}={g}_{p}(1-{f}_{t})(1-{f}_{p}){\lambda}_{1}S-{\nu}_{u,p}{I}_{u,p}-{k}_{u,p}{\lambda}_{2}{I}_{u,p}\\ {\stackrel{.}{I}}_{t,n}=(1-{g}_{p})({f}_{t}(1-{f}_{p})+{f}_{p}(1-{e}_{p})){\lambda}_{1}S-{\nu}_{t,n}{I}_{t,n}-{k}_{t,n}{\lambda}_{2}{I}_{t,n}\\ {\stackrel{.}{I}}_{t,p}={g}_{p}({f}_{t}(1-{f}_{p})+{f}_{p}(1-{e}_{p})){\lambda}_{1}S-{\nu}_{t,p}{I}_{t,p}-{k}_{t,p}{\lambda}_{2}{I}_{t,p}\\ {\stackrel{.}{I}}_{r,n}=(1-{g}_{p}){\lambda}_{r}S-{\nu}_{r,n}{I}_{r,n}-{k}_{r,n}{\lambda}_{2}{I}_{r,n}\\ {\stackrel{.}{I}}_{r,p}={g}_{p}{\lambda}_{r}S-{\nu}_{r,p}{I}_{r,p}-{k}_{r,p}{\lambda}_{2}{I}_{r,p}\\ {\stackrel{.}{B}}_{u,u}=(1-{f}_{t})(1-{g}_{t})({\nu}_{u,n}{c}_{u,n}+{k}_{u,n}{\lambda}_{2}){I}_{u,n}-{\delta}_{u,u}{B}_{u,u}\\ {\stackrel{.}{B}}_{u,t}=(1-{f}_{t}){g}_{t}({\nu}_{u,n}{c}_{u,n}+{k}_{u,n}{\lambda}_{2}){I}_{u,n}+(1-{f}_{t})({\nu}_{u,p}{c}_{u,p}+{k}_{u,p}{\lambda}_{2}){I}_{u,p}-{\delta}_{u,t}{B}_{u,t}\\ {\stackrel{.}{B}}_{t,u}={f}_{t}(1-{g}_{t})({\nu}_{u,n}{c}_{u,n}+{k}_{u,n}{\lambda}_{2}){I}_{u,n}+(1-{g}_{t})({\nu}_{t,n}{c}_{t,n}+{k}_{t,n}{\lambda}_{2}){I}_{t,n}-{\delta}_{t,u}{B}_{t,u}\\ {\stackrel{.}{B}}_{t,t}={f}_{t}{g}_{t}({\nu}_{u,n}{c}_{u,n}+{k}_{u,n}{\lambda}_{2}){I}_{u,n}+{f}_{t}({\nu}_{u,p}{c}_{u,p}+{k}_{u,p}{\lambda}_{2}){I}_{u,p}+{g}_{t}({\nu}_{t,n}{c}_{t,n}+{k}_{t,n}{\lambda}_{2}){I}_{t,n}\\ +({\nu}_{t,p}{c}_{t,p}+{k}_{t,p}{\lambda}_{2}){I}_{t,p}-{\delta}_{t,t}{B}_{t,t}\\ {\stackrel{.}{B}}_{r,u}=(1-{g}_{t})({\nu}_{r,n}{c}_{r,n}+{k}_{r,n}{\lambda}_{2}){I}_{r,n}^{7}-{\delta}_{r,u}{B}_{r,u}\\ {\stackrel{.}{B}}_{r,t}={g}_{t}({\nu}_{r,n}{c}_{r,n}+{k}_{r,n}{\lambda}_{2}){I}_{r,n}+({\nu}_{r,p}{c}_{r,p}+{k}_{r,p}{\lambda}_{2}){I}_{r,p}-{\delta}_{r,t}{B}_{r,t}\end{array}$$

The set of deterministic ordinary differential equations described above was implemented in Matlab R2007a (The Mathworks). The code is available from the authors. For each of the different scenarios described in the results section, we simulated 10000 pandemic outbreaks with different values for the model parameters. Parameter sampling was performed using Latin Hypercube Sampling (LHS) (7), we assumed uniform distributions of the parameters in the ranges given in Tables 2–4. To assess the influence of different parameters on the results, we performed sensitivity analyses (40, 63). Both the Latin Hypercube sampling and sensitivity analysis were performed using SaSAT (40). Unless otherwise stated, we assume that intervention starts after 500 influenza cases have occurred.

We use the mathematical model to investigate how different levels of AV and AB prophylaxis or treatment affect the number of influenza and bacteria infected cases, the peak number of cases, and the number of deaths during an influenza pandemic in the U.S. We start with a model in which only commensal bacteria that colonized a host before influenza infection cause secondary bacterial infections. Next, we consider a scenario where both influenza and bacteria infected hosts can spread bacteria or virus. We then investigate how the emergence or pre-existence of AV resistant influenza changes the effect of the different intervention strategies. We further show how differences in the delay time before control starts affect the results, and how results change for a less severe pandemic. Lastly, we perform a sensitivity analysis to determine which parameters have the most impact on the outcomes.

Commensal bacteria are likely to be an important source for secondary bacterial infections. We therefore start out with a model in which influenza infected hosts can develop secondary bacterial infections through the invasion of commensal bacteria that already reside in the host, but neither influenza nor bacteria infected hosts are assumed to spread bacteria (*α _{i}*

Figure 2 shows the time course of 100 simulated infections for the baseline scenario with no AV or AB intervention strategies. The chosen values for *R*_{0} and death rates (see Tables 2 and and3)3) lead to a total fraction of infecteds of ≈ 70 – 90%. This number is solely dictated by the range of *R*_{0} values we used. Data from most influenza outbreaks suggests that the fraction of infecteds is lower than what would be expected solely based on the value of *R*_{0}. The discrepancy is likely due to both the fact that we assume that every infection is symptomatic and the fact that our model assumes a homogeneous population. The percentage of deaths goes up to ≈ 5%. This represents a rather severe outbreak with deaths similar to those seen in the 1918 pandemic. We use this setting as a “worst case” scenario. We will discuss a situation that is more like the 2009 pandemic below.

Time course of 100 simulated infections. Left: Susceptibles (green), Influenza infected (blue) and Bacteria infected (red), expressed as percent of the total population. Right: Total Deaths due to Influenza (blue) and Bacteria (red) infections, expressed **...**

Next, we investigate how different intervention strategies (IS) based on AV or AB treatment or prophylaxis reduce the number of total and bacterial cases, the peak number of cases, and the number of deaths compared to the baseline scenario without interventions (Fig. 3). We find that AV treatment (IS1) reduces the number of both influenza and secondary bacterial infections and the mortality by ≈ 25%, while the peak number of cases is reduced by about 50%. If AV prophylaxis is added (IS2), the reduction in cases and the reduction in mortality increases. As expected, AB treatment added to AV treatment (IS3) does not lead to an additional reduction in influenza or bacteria cases compared to IS1, but does much better in reducing death compared to IS1 and IS2. AB prophylaxis in addition to AV treatment (IS4) also does not reduce influenza cases or the peak total cases, but prevents additional bacteria cases – the type of cases that are most likely to be hospitalized. Reduction of mortality is somewhat lower for IS4 than is achieved by IS3. Combining AB prophylaxis and treatment on top of AV treatment (IS5) leads to a reduction in mortality that is similar to IS3. Overall, adding AB control strategies does little to reduce the total number of cases but is effective in reducing mortality.

It is possible that hosts with an influenza infection who are carriers of commensal bacteria start to spread those bacteria (5, 11, 12, 82). Further, bacteria infected hosts might also spread bacteria. Additionally, while some evidence seems to suggest that hosts infected with bacteria do not simultaneously have high viral titers (60, 95), in at least some situations, virus was reported to be found together with bacteria (60, 64), or bacterial infection increased viral load in animal models (78). It is therefore also possible that bacteria infected hosts still harbor and spread influenza. We now investigate such a situation where both influenza and bacteria infected hosts can spread both pathogens. Figure 4 shows the reduction in cases and mortality for the the same five intervention strategies as considered in Figure 3. Overall, the results are similar to those seen in Figure 3; the reduction in bacteria cases and mortality increases somewhat. We also investigated situations where only influenza infecteds transmit both pathogens or influenza infecteds transmit both pathogens but bacteria infecteds only transmit bacteria. The results for such scenarios are very similar to Figures 3 and and44 (not shown).

Previous studies have suggested that during a large outbreak and extensive AV use, the emergence of resistance to anti-influenza drugs, such as the neuraminidase inhibitors, is likely (2, 10, 34, 55, 80). Indeed, the first few cases of drug resistance for the 2009 H1N1 strain have already been reported (1). Here, we consider how emergence or pre-existence of AV drug resistance impacts the usefulness of AV or AB intervention strategies. While AB resistance is certainly a serious problem (17, 52, 54, 56, 69), the relatively short timescale of an influenza pandemic makes it probable that AB drugs that are effective at the beginning of the pandemic outbreak remain effective for the duration of the outbreak. We therefore assume that bacteria remain sensitive throughout the outbreak to the drugs being used for AB control and only consider AV resistance. In one scenario, we assume that AV resistance does not pre-exist but emerges during the pandemic. Alternatively, it might be possible that by the time a pandemic influenza virus reaches the U.S., a certain fraction of the infected hosts already harbor an influenza strain that is resistant to AV drugs. We therefore also consider a scenario where an influenza strain resistant to the AV drugs already exists at a low frequency at the beginning of the pandemic.

Figure 5 shows results for the same situation as shown in Figure 4, but now with the inclusion of AV resistant virus. Not unexpectedly, the AV control strategies perform worse in the presence of resistance. This is most noticeable for AV prophylaxis. The reason for this is that AV prophylaxis reduces the fitness of the drug sensitive strain enough for the resistant strain to quickly emerge and to cause a strong and uncontrolled “second wave” (19, 33, 34, 55, 72). In contrast, the different AB strategies are little affected and IS3-IS5 are still able to prevent a significant amount of mortality, similar to the levels for the situation without resistant virus present.

So far, we assumed that intervention starts after 500 infected cases have occurred. This assumes that the time it takes to determine that an outbreak is occurring and the logistics to get the intervention measures implemented is rather short. With regard to the 2009 pandemic, rapid intervention on a global scale is certainly not possible anymore – though it might still be possible for localized outbreaks. In any case, it is worth investigating how changes in the time lag before intervention start affect the results. In Figure 6, we consider scenarios where intervention starts later, after either 1% or 10% of the population have already been infected. The 1% scenario leads to results that are almost identical to the rapid intervention scenario (compare Fig. 6 left with Fig. 4), while the effectiveness of control is reduced for the 10% scenario. Overall, and somewhat encouragingly, these results suggest that some delay in implementing the control strategies is tolerable and does not impact their effectiveness too much. However, we want to point out that the actual biological transmission process is stochastic, and that a stochastic model favors early intervention more heavily, as we discussed previously (34).

So far we assumed a situation where the influenza virus has a relatively high *R*_{0} and the percentage of deaths is comparable to the severe 1918 outbreak. Given the currently ongoing 2009 H1N1 pandemic with its lower *R*_{0} and mortality that seems not much higher than seasonal strains (22, 23, 42, 93), we decided to investigate the impact of the various control strategies in such a situation. We consider scenarios with both absence and presence of AV resistance. As Figure 7 shows, the different control strategies have an increased impact with regard to reduction of cases (compare Fig. 7 left with Fig. 4 and Fig. 7 right with Fig. 5 left). For this scenario, the AB based control strategies, IS3-IS5, show little improvement over IS1, even for the reduction in mortality. This is not too surprising since we assumed for the 2009-like scenario both a lower *R*_{0} and that most deaths are *not* due to secondary bacterial infections (see Tables 2 and and3)3) – hence the obvious reduction in importance of AB strategies. Drug resistance emergence has again the expected effect, namely lowering the impact of AV strategies.

Our model contains many parameters that are not very well known. We therefore performed a large number of simulations for different values of the parameters. In this section, we describe results from a sensitivity analysis that helps to understand the impact of different parameters on the results presented in the previous sections. We focused on the scenario with transmission of bacteria and virus and no drug resistance, i.e. the scenario shown in Figure 4. We computed partial rank correlation coefficients (PRCC) (40, 63) for the different outcomes (reduction of total, bacteria and peak cases and reduction of deaths) and the different intervention strategies.

Table 5 summarizes the results for the most influential parameters for a given output and IS. As can be seen, the influenza transmission parameter *β _{u,n}* has by far the largest impact on the results, mainly because it drives the overall outbreak dynamics. (Note that the parameters

We also looked at the PRCC for other scenarios, specifically the scenario with antiviral resistance present (Fig. 5 right) and the 2009-like scenario (Fig. 7 left). We do not show the PRCC tables for those scenarios since they add little further insight, but briefly mention the main findings: As one might expect, for the situation with antiviral resistance present, the parameter describing transmission of the resistance strain, *β _{r,n}* becomes very important, analogous to the importance of

For PRCC to be meaningful, the results need to depend monotonically on the parameters. We checked this by investigating scatterplots for the most influential parameters from Table 5. We found monotonicity in all instances. Figure 8 shows example scatterplots for four different parameters. Other parameters lead to similar results (not shown).

We studied AV and AB intervention strategies using a mathematical model that explicitly included bacterial infection and potential bacteria transmission. Overall, we find that AB intervention strategies do not lead to significant reduction in the total number of cases – even for the situation where bacteria infected hosts can transmit both virus and bacteria. However, AB control measures help to reduce the number of bacterial infections – which are more likely in need of medical attention or hospitalization. Additionally, AB treatment or prophylaxis can significantly reduce mortality. This is achieved by specifically targeting bacteria infected hosts that have a high risk of mortality – a different mechanism than the reduction of deaths through prevention of total cases that AV prophylaxis can bring about.

Not unexpectedly, the role of AB intervention becomes especially important if we consider the possibility that resistance renders AV drugs useless, which could occur early in the infection, for instance if initial containment strategies generate a resistant strain that can spread easily. Obviously, if a significant fraction of the bacteria were resistant to the AB intervention, this would diminish their effectiveness. For instance if half of the population harbored bacteria that were resistant to the administered drugs, it would in effect represent an intervention strategy with the level of AB treatment or prophylaxis reduced by half (if the latter is based on drugs, not vaccines). However, while AB resistance is a serious issue, it is reasonable to assume that any AB drug that has been found effective against bacteria at the beginning of a pandemic outbreak will remain effective for the comparably short duration of the outbreak. Future studies might want to focus on the potential impact of AB resistance. As expected, we find that if mortality due to bacterial infections is low (a 2009-like scenario), the impact of AB control strategies is reduced.

As is the case with any mathematical model, ours includes a number of simplifying assumptions. The main assumption inherent in the model formulation is the homogeneity of the population. Hosts are categorized by their infection status but not further. More detailed models could take into account different age classes, possible spatial structure, and other details. Further, we ignored asymptomatic infections, we assumed that hosts always need to be infected with influenza before they can harbor a bacterial infection, and we ignored mixed drug sensitive and drug resistant virus infections. Also inherent in the model formulation is the assumption that infectious periods are exponentially distributed. It is known that relaxing this assumption can sometimes change results (57, 88). Our model uncertainty came from sampling of parameters, we ignored the inherently stochastic nature of the transmission process. While this is likely justified for the dynamics of the drug sensitive virus, the resistant strain might require stochastic treatment (34, 35). For bacterial infections, it is not clear how important stochastic effects might be, but experimental data suggest that secondary bacterial infections often occur in heterogeneous clusters (11, 12).

Clearly, our model is only the first step towards more detailed models that could be used to study the dynamics of co-infection (20, 21, 25, 29, 59). However, it seems currently not very useful to try and implement a more complicated model. This is because many of the parameters even for our relatively simple model are poorly known. While we used reports from the existing literature to estimate parameters, often the reported data are so vague that our estimates are mostly educated guesses. A more complicated model would simply exacerbate the problem of unknown parameter values. As our sensitivity analysis shows, some of the poorly known parameters affect the results by a lot. While the transmission rate of influenza (*β _{u,n}*) is usually relatively well known, this is not the case for the transmission rates of bacteria (

In summary, we have built and analyzed what seems to be the first model that explicitly considers bacterial infections and the use of both antiviral and antibacterial intervention strategies during an influenza pandemic. We find that while antibacterial intervention strategies are unlikely to play an important role in reducing the overall number of cases, such interventions can lead to a significant reduction in mortality and in the number of bacterial infections. We consider our study a first step towards exploring the role of antiviral and antibacterial control strategies in preventing cases and deaths during an influenza pandemic. While the lack of precision in our results precludes precise predictions based on our model, the qualitative findings are robust for the different scenarios we investigate. Our study therefore lends further support to previous suggestions that pandemic preparedness plans should not only include AV and non-pharmacological intervention strategies, but also include intervention strategies based on AB treatment or prophylaxis – in the form of both drugs and vaccines – as part of the overall influenza control strategy (8, 11, 12, 67, 74).

This work was partially supported by the National Institute of General Medical Sciences MIDAS grant U01-GM070749.

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1. Oseltamivir-resistant novel influenza a (h1n1) virus infection in two immunosuppressed patients — seattle, washington, 2009. MMWR. 2009;58:893–896. [PubMed]

2. Alexander ME, Bowman CS, Feng Z, Gardam M, Moghadas SM, Rst G, Wu J, Yan P. Emergence of drug resistance: implications for antiviral control of pandemic influenza. Proc Biol Sci. Jul, 2007. pp. 1675–1684. URL http://dx.doi.org/10.1098/rspb.2007.0422. [PMC free article] [PubMed]

3. Anderson RM, May RM. Infectious Diseases of Humans -Dynamics and Control. Oxford Science Publications; Oxford: 1991.

4. Aoki FY, Macleod MD, Paggiaro P, Carewicz O, Sawy AE, Wat C, Griffiths M, Waalberg E, Ward P, Group IMPACTS. Early administration of oral oseltamivir increases the benefits of influenza treatment. J Antimicrob Chemother. 2003 Jan;51 (1):123–129. [PubMed]

5. Bassetti S, Bischoff WE, Walter M, Bassetti-Wyss BA, Mason L, Reboussin BA, D’Agostino RB, Gwaltney JM, Pfaller MA, Sherertz RJ. Dispersal of staphylococcus aureus into the air associated with a rhinovirus infection. Infect Control Hosp Epidemiol. 2005 Feb;26 (2):196–203. [PubMed]

6. Bhat N, Wright JG, Broder KR, Murray EL, Greenberg ME, Glover MJ, Likos AM, Posey DL, Klimov A, Lindstrom SE, Balish A, jo Medina M, Wallis TR, Guarner J, Paddock CD, Shieh WJ, Zaki SR, Sejvar JJ, Shay DK, Harper SA, Cox NJ, Fukuda K, Uyeki TM, Team ISI. Influenza-associated deaths among children in the united states, 2003–2004. N Engl J Med. 2005 Dec;353 (24):2559–2567. [PubMed]

7. BLOWER SM, DOWLATABADI H. Sensitivity and uncertainty analysis of complex-models of disease transmission - an hiv model, as an example. International Statistical Review. 1994 Aug;62 (2):229–243.

8. Bonten MJM, Prins JM. Antibiotics in pandemic flu. BMJ. Feb, 2006. pp. 248–249. URL http://dx.doi.org/10.1136/bmj.332.7536.248. [PMC free article] [PubMed]

9. BRIMBLECOMBE FS, CRUICKSHANK R, MASTERS PL, REID DD, STEWART GT. Family studies of respiratory infections. Br Med J. 1958 Jan;1 (5063):119–128. [PMC free article] [PubMed]

10. Brockmann SO, Schwehm M, Duerr H-P, Witschi M, Koch D, Vidondo B, Eichner M. Modeling the effects of drug resistant influenza virus in a pandemic. Virol J. 2008. p. 133. URL http://dx.doi.org/10.1186/1743-422X-5-133. [PMC free article] [PubMed]

11. Brundage JF. Interactions between influenza and bacterial respiratory pathogens: implications for pandemic preparedness. Lancet Infect Dis. May, 2006. pp. 303–312. URL http://dx.doi.org/10.1016/S1473-3099(0670466-2) [PubMed]

12. Brundage JF, Shanks GD. Deaths from bacterial pneumonia during 1918–19 influenza pandemic. Emerg Infect Dis. 2008 Aug;14 (8):1193–1199. [PMC free article] [PubMed]

13. Carrat F, Schwarzinger M, Housset B, Valleron AJ. Antibiotic treatment for influenza does not affect resolution of illness, secondary visits or lost workdays. Eur J Epidemiol. 2004;19 (7):703–705. [PubMed]

14. Carrat F, Vergu E, Ferguson NM, Lemaitre M, Cauchemez S, Leach S, Valleron A-J. Time lines of infection and disease in human influenza: a review of volunteer challenge studies. Am J Epidemiol. Apr, 2008. pp. 775–785. URL http://dx.doi.org/10.1093/aje/kwm375. [PubMed]

15. Cartwright KA, Jones DM, Smith AJ, Stuart JM, Kaczmarski EB, Palmer SR. Influenza a and meningococcal disease. Lancet. 1991 Aug;338 (8766):554–557. [PubMed]

16. Cole JA, Loughlin JE, Ajene AN, Rosenberg DM, Cook SE, Walker AM. The effect of zanamivir treatment on influenza complications: a retrospective cohort study. Clin Ther. 2002 Nov;24 (11):1824–1839. [PubMed]

17. Dancer SJ. How antibiotics can make us sick: the less obvious adverse effects of antimicrobial chemotherapy. Lancet Infectious Diseases. 2004;4:611–619. [PubMed]

18. Diekema DJ, Jones RN. Oxazolidinone antibiotics. Lancet. Dec, 2001. pp. 1975–1982. URL http://dx.doi.org/10.1016/S0140-6736(0106964-1) [PubMed]

19. Eichner M, Schwehm M, Duerr H-P, Witschi M, Koch D, Brockmann SO, Vidondo B. Antiviral prophylaxis during pandemic influenza may increase drug resistance. BMC Infect Dis. 2009. p. 4. URL http://dx.doi.org/10.1186/1471-2334-9-4. [PMC free article] [PubMed]

20. Ferguson NM, Cummings DAT, Cauchemez S, Fraser C, Riley S, Meeyai A, Iamsirithaworn S, Burke DS. Strategies for containing an emerging influenza pandemic in Southeast Asia. Nature. 2005 Sep;437 (7056):209–214. [PubMed]

21. Ferguson NM, Cummings DAT, Fraser C, Cajka JC, Cooley PC, Burke DS. Strategies for mitigating an influenza pandemic. Nature. Jul, 2006. pp. 448–452. URL http://dx.doi.org/10.1038/nature04795. [PubMed]

22. for Disease Control, C., Prevention, 2009. 2009 h1n1 flu. 2009. URL http://www.cdc.gov/h1n1flu/

23. Fraser C, Donnelly CA, Cauchemez S, Hanage WP, Kerkhove MDV, Hollingsworth TD, Griffin J, Baggaley RF, Jenkins HE, Lyons EJ, Jombart T, Hinsley WR, Grassly NC, Balloux F, Ghani AC, Ferguson NM, Rambaut A, Pybus OG, Lopez-Gatell H, Alpuche-Aranda CM, Chapela IB, Zavala EP, Guevara DME, Checchi F, Garcia E, Hugonnet S, Roth C, Collaboration WHORPA. Pandemic potential of a strain of influenza a (h1n1): early findings. Science. 2009 Jun;324 (5934):1557–1561. [PMC free article] [PubMed]

24. Garske T, Legrand J, Donnelly CA, Ward H, Cauchemez S, Fraser C, Ferguson NM, Ghani AC. Assessing the severity of the novel influenza a/h1n1 pandemic. BMJ. 2009;339:b2840. [PubMed]

25. Germann TC, Kadau K, Longini IM, Macken CA. Mitigation strategies for pandemic influenza in the United States. Proc Natl Acad Sci U S A. 2006 Apr;103 (15):5935–5940. [PubMed]

26. Grabowska K, Hgberg L, Penttinen P, Svensson A, Ekdahl K. Occurrence of invasive pneumococcal disease and number of excess cases due to influenza. BMC Infect Dis. 2006. p. 58. URL http://dx.doi.org/10.1186/1471-2334-6-58. [PMC free article] [PubMed]

27. Gupta RK, George R, Nguyen-Van-Tam JS. Bacterial pneumonia and pandemic influenza planning. Emerg Infect Dis. 2008 Aug;14 (8):1187–1192. [PMC free article] [PubMed]

28. Hageman JC, Uyeki TM, Francis JS, Jernigan DB, Wheeler JG, Bridges CB, Barenkamp SJ, Sievert DM, Srinivasan A, Doherty MC, McDougal LK, Killgore GE, Lopatin UA, Coffman R, MacDonald JK, McAllister SK, Fosheim GE, Patel JB, McDonald LC. Severe community-acquired pneumonia due to staphylococcus aureus, 2003–04 influenza season. Emerg Infect Dis. 2006 Jun;12 (6):894–899. [PMC free article] [PubMed]

29. Halloran ME, Ferguson NM, Eubank S, Longini IM, Cummings DAT, Lewis B, Xu S, Fraser C, Vullikanti A, Germann TC, Wagener D, Beckman R, Kadau K, Barrett C, Macken CA, Burke DS, Cooley P. Modeling targeted layered containment of an influenza pandemic in the United States. Proc Natl Acad Sci U S A. Mar, 2008. pp. 4639–4644. URL http://dx.doi.org/10.1073/pnas.0706849105. [PubMed]

30. Halloran ME, Hayden FG, Yang Y, Longini IM, Monto AS. Antiviral effects on influenza viral transmission and pathogenicity: observations from household-based trials. Am J Epidemiol. Jan, 2007. pp. 212–221. URL http://dx.doi.org/10.1093/aje/kwj362. [PubMed]

31. Hament JM, Kimpen JL, Fleer A, Wolfs TF. Respiratory viral infection predisposing for bacterial disease: a concise review. FEMS Immunol Med Microbiol. 1999 Dec;26 (3–4):189–195. [PubMed]

32. Handel A, Longini IM, Antia R. Neuraminidase Inhibitor Resistance in Influenza: Assessing the Danger of Its Generation and Spread. PLoS Comput Biol. Dec, 2007. p. e240. URL http://dx.doi.org/10.1371/journal.pcbi.0030240. [PubMed]

33. Handel A, Longini IM, Antia R. What is the best control strategy for multiple infectious disease outbreaks? Proc Biol Sci. Mar, 2007. pp. 833–837. URL http://dx.doi.org/10.1098/rspb.2006.0015. [PMC free article] [PubMed]

34. Handel A, Longini IM, Antia R. Antiviral resistance and the control of pandemic influenza: the roles of stochasticity, evolution and model details. J Theor Biol. Jan, 2009. pp. 117–125. URL http://dx.doi.org/10.1016/j.jtbi.2008.09.021. [PMC free article] [PubMed]

35. Handel A, Regoes RR, Antia R. The role of compensatory mutations in the emergence of drug resistance. PLoS Comput Biol. Oct, 2006. p. e137. URL http://dx.doi.org/10.1371/journal.pcbi.0020137. [PubMed]

36. Hayden FG, Treanor JJ, Fritz RS, Lobo M, Betts RF, Miller M, Kinnersley N, Mills RG, Ward P, Straus SE. Use of the oral neuraminidase inhibitor oseltamivir in experimental human influenza: randomized controlled trials for prevention and treatment. JAMA. 1999 Oct;282 (13):1240–1246. [PubMed]

37. Herlocher ML, Truscon R, Elias S, Yen H-L, Roberts NA, Ohmit SE, Monto AS. Influenza viruses resistant to the antiviral drug oseltamivir: transmission studies in ferrets. J Infect Dis. Nov, 2004. pp. 1627–1630. URL http://dx.doi.org/10.1086/424572. [PubMed]

38. HERS JF, MASUREL N, MULDER J. Bacteriology and histopathology of the respiratory tract and lungs in fatal asian influenza. Lancet. 1958 Nov;2 (7057):1141–1143. [PubMed]

39. Hethcote HW. The Mathematics of Infectious Diseases. SIAM Review. 2000;42:599–653.

40. Hoare A, Regan DG, Wilson DP. Sampling and sensitivity analyses tools (sasat) for computational modelling. Theor Biol Med Model. 2008. p. 4. URL http://dx.doi.org/10.1186/1742-4682-5-4. [PMC free article] [PubMed]

41. Hoti F, Erst P, Leino T, Auranen K. Outbreaks of streptococcus pneumoniae carriage in day care cohorts in finland - implications for elimination of transmission. BMC Infect Dis. 2009. p. 102. URL http://dx.doi.org/10.1186/1471-2334-9-102. [PMC free article] [PubMed]

42. Jamieson DJ, Honein MA, Rasmussen SA, Williams JL, Swerdlow DL, Biggerstaff MS, Lindstrom S, Louie JK, Christ CM, Bohm SR, Fonseca VP, Ritger KA, Kuhles DJ, Eggers P, Bruce H, Davidson HA, Lutterloh E, Harris ML, Burke C, Cocoros N, Finelli L, MacFarlane KF, Shu B, Olsen SJ. Group NIAHPW. H1n1 2009 influenza virus infection during pregnancy in the usa. Lancet. Aug, 2009. pp. 451–458. URL http://dx.doi.org/10.1016/S0140-6736(0961304-0) [PubMed]

43. Jennings LC, Anderson TP, Beynon KA, Chua A, Laing RTR, Werno AM, Young SA, Chambers ST, Murdoch DR. Incidence and characteristics of viral community-acquired pneumonia in adults. Thorax. Jan, 2008. pp. 42–48. URL http://dx.doi.org/10.1136/thx.2006.075077. [PubMed]

44. Juven T, Mertsola J, Waris M, Leinonen M, Meurman O, Roivainen M, Eskola J, Saikku P, Ruuskanen O. Etiology of community-acquired pneumonia in 254 hospitalized children. Pediatr Infect Dis J. 2000 Apr;19 (4):293–298. [PubMed]

45. Kaiser L, Fritz RS, Straus SE, Gubareva L, Hayden FG. Symptom pathogenesis during acute influenza: interleukin-6 and other cytokine responses. J Med Virol. 2001 Jul;64 (3):262–268. [PubMed]

46. Kaiser L, Keene ON, Hammond JM, Elliott M, Hayden FG. Impact of zanamivir on antibiotic use for respiratory events following acute influenza in adolescents and adults. Arch Intern Med. 2000 Nov;160 (21):3234–3240. [PubMed]

47. Kaiser L, Lew D, Hirschel B, Auckenthaler R, Morabia A, Heald A, Benedict P, Terrier F, Wunderli W, Matter L, Germann D, Voegeli J, Stalder H. Effects of antibiotic treatment in the subset of common-cold patients who have bacteria in nasopharyngeal secretions. Lancet. 1996 Jun;347 (9014):1507–1510. [PubMed]

48. Kaiser L, Wat C, Mills T, Mahoney P, Ward P, Hayden F. Impact of oseltamivir treatment on influenza-related lower respiratory tract complications and hospitalizations. Arch Intern Med. Jul, 2003. pp. 1667–1672. URL http://dx.doi.org/10.1001/archinte.163.14.1667. [PubMed]

49. Kajita E, Okano JT, Bodine EN, Layne SP, Blower S. Modelling an outbreak of an emerging pathogen. Nat Rev Microbiol. Sep, 2007. pp. 700–709. URL http://dx.doi.org/10.1038/nrmicro1660. [PubMed]

50. Klugman KP, Astley CM, Lipsitch M. Time from illness onset to death, 1918 influenza and pneumococcal pneumonia. Emerg Infect Dis. 2009 Feb;15 (2):346–347. [PMC free article] [PubMed]

51. Klugman KP, Madhi SA. Pneumococcal vaccines and flu preparedness. Science. 2007 Apr;316(5821):49–50. URL http://dx.doi.org/10.1126/science.316.5821.49c. [PubMed]

52. Levy SB, Marshall B. Antibacterial resistance worldwide: causes, challenges and responses. Nature Medicine. 2004;10 (12):S122–S129. [PubMed]

53. Lim WS, Macfarlane JT, Boswell TC, Harrison TG, Rose D, Leinonen M, Saikku P. Study of community acquired pneumonia aetiology (scapa) in adults admitted to hospital: implications for management guidelines. Thorax. 2001 Apr;56 (4):296–301. [PMC free article] [PubMed]

54. Lipsitch M. The rise and fall of antimicrobial resistance. Trends in Microbiology. 2001;9 (9):438–444. [PubMed]

55. Lipsitch M, Cohen T, Murray M, Levin BR. Antiviral Resistance and the Control of Pandemic Influenza. PLoS Med. Jan, 2007. p. e15. URL http://dx.doi.org/10.1371/journal.pmed.0040015. [PubMed]

56. Livermore DM. Minimising antibiotic resistance. Lancet Infect Dis. Jul, 2005. pp. 450–459. URL http://dx.doi.org/10.1016/S1473-3099(0570166-3) [PubMed]

57. Lloyd AL. Destabilization of epidemic models with the inclusion of realistic distributions of infectious periods. Proceedings of the Royal Society London B. 2001;268:985–993. [PMC free article] [PubMed]

58. Longini IM, Halloran ME. Strategy for distribution of influenza vaccine to high-risk groups and children. Am J Epidemiol. Feb, 2005. pp. 303–306. URL http://dx.doi.org/10.1093/aje/kwi053. [PubMed]

59. Longini IM, Nizam A, Xu S, Ungchusak K, Hanshaoworakul W, Cummings DAT, Halloran ME. Containing pandemic influenza at the source. Science. 2005 Aug;309 (5737):1083–1087. [PubMed]

60. Louria DB, Blumenfeld HL, Ellis JT, Kilbourne ED, Rogers DE. Studies on influenza in the pandemic of 1957–1958. II. Pulmonary complications of influenza. J Clin Invest. Jan, 1959. pp. 213–265. URL http://dx.doi.org/10.1172/JCI103791. [PMC free article] [PubMed]

61. Madhi SA, Klugman KP, Group VT. A role for streptococcus pneumoniae in virus-associated pneumonia. Nat Med. 2004 Aug;10 (8):811–813. [PubMed]

62. Maeda S, Yamada Y, Nakamura H, Maeda T. Efficacy of antibiotics against influenza-like illness in an influenza epidemic. Pediatr Int. 1999 Jun;41 (3):274–276. [PubMed]

63. Marino S, Hogue IB, Ray CJ, Kirschner DE. A methodology for performing global uncertainty and sensitivity analysis in systems biology. J Theor Biol. Sep, 2008. pp. 178–196. URL http://dx.doi.org/10.1016/j.jtbi.2008.04.011. [PMC free article] [PubMed]

64. Maxwell ES, Ward TG, Metre TEV. The relation of influenza virus and bacteria in the etiology of pneumonia. J Clin Invest. 1949 Mar;28(2):307–318. URL http://dx.doi.org/10.1172/JCI102073. [PMC free article] [PubMed]

65. McCullers JA. Effect of antiviral treatment on the outcome of secondary bacterial pneumonia after influenza. J Infect Dis. Aug, 2004. pp. 519–526. URL http://dx.doi.org/10.1086/421525. [PubMed]

66. McCullers JA. Insights into the interaction between influenza virus and pneumococcus. Clin Microbiol Rev. Jul, 2006. pp. 571–582. URL http://dx.doi.org/10.1128/CMR.00058-05. [PMC free article] [PubMed]

67. McCullers JA. Planning for an influenza pandemic: Thinking beyond the virus. J Infect Dis. Oct, 2008. pp. 945–947. URL http://dx.doi.org/10.1086/592165. [PMC free article] [PubMed]

68. McCullers JA, Bartmess KC. Role of neuraminidase in lethal synergism between influenza virus and streptococcus pneumoniae. J Infect Dis. 2003 Mar;187 (6):1000–1009. [PubMed]

69. Memoli MJ, Morens DM, Taubenberger JK. Pandemic and seasonal influenza: therapeutic challenges. Drug Discov Today. Jul, 2008. pp. 590–595. URL http://dx.doi.org/10.1016/j.drudis.2008.03.024. [PMC free article] [PubMed]

70. Mills CE, Robins JM, Lipsitch M. Transmissibility of 1918 pandemic influenza. Nature. 2004 Dec;432 (7019):904–906. [PubMed]

71. Moberley SA, Holden J, Tatham DP, Andrews RM. Vaccines for preventing pneumococcal infection in adults. Cochrane Database Syst Rev. 2008. p. CD000422. URL http://dx.doi.org/10.1002/14651858.CD000422.pub2. [PubMed]

72. Moghadas SM, Bowman CS, Rst G, Wu J. Population-wide emergence of antiviral resistance during pandemic influenza. PLoS ONE. 2008. p. e1839. URL http://dx.doi.org/10.1371/journal.pone.0001839. [PMC free article] [PubMed]

73. Morens DM, Fauci AS. The 1918 influenza pandemic: insights for the 21st century. J Infect Dis. Apr, 2007. pp. 1018–1028. URL http://dx.doi.org/10.1086/511989. [PubMed]

74. Morens DM, Taubenberger JK, Fauci AS. Predominant role of bacterial pneumonia as a cause of death in pandemic influenza: implications for pandemic influenza preparedness. J Infect Dis. Oct, 2008. pp. 962–970. URL http://dx.doi.org/10.1086/591708. [PMC free article] [PubMed]

75. Moscona A. Neuraminidase inhibitors for influenza. N Engl J Med. Sep, 2005. pp. 1363–1373. URL http://dx.doi.org/10.1056/NEJMra050740. [PubMed]

76. Nicholson KG. Blackwell Science, Ch. Human Influenza. 1998. Textbook of Influenza; pp. 219–264.

77. O’Brien KL, Walters MI, Sellman J, Quinlisk P, Regnery H, Schwartz B, Dowell SF. Severe pneumococcal pneumonia in previously healthy children: the role of preceding influenza infection. Clin Infect Dis. 2000 May;30 (5):784–789. [PubMed]

78. Okamoto S, Kawabata S, Nakagawa I, Okuno Y, Goto T, Sano K, Hamada S. Influenza a virus-infected hosts boost an invasive type of streptococcus pyogenes infection in mice. J Virol. 2003 Apr;77 (7):4104–4112. [PMC free article] [PubMed]

79. Pourbohloul B, Ahued A, Davoudi B, Meza R, Meyers LA, Skowronski DM, Villaseor I, Galvn F, Cravioto P, Earn DJD, Dushoff J, Fisman D, Edmunds WJ, Hupert N, Scarpino SV, Trujillo J, Lutzow M, Morales J, Contreras A, Chvez C, Patrick DM, Brunham RC. Initial human transmission dynamics of the pandemic (h1n1) 2009 virus in north america. Influenza Other Respi Viruses. Sep, 2009. pp. 215–222. URL http://dx.doi.org/10.1111/j.1750-2659.2009.00100.x. [PMC free article] [PubMed]

80. Regoes RR, Bonhoeffer S. Emergence of drug-resistant influenza virus: population dynamical considerations. Science. 2006 Apr;312 (5772):389–391. [PubMed]

81. Shann F, Gratten M, Germer S, Linnemann V, Hazlett D, Payne R. Aetiology of pneumonia in children in goroka hospital, papua new guinea. Lancet. 1984 Sep;2 (8402):537–541. [PubMed]

82. Sheretz RJ, Reagan DR, Hampton KD, Robertson KL, Streed SA, Hoen HM, Thomas R, Gwaltney JM. A cloud adult: the Staphylococcus aureus-virus interaction revisited. Ann Intern Med. 1996 Mar;124 (6):539–547. [PubMed]

83. Soper GA. The influenza pneumonia pandemic in the american army camps during september and october. 1918. Science. Nov, 1918. pp. 451–456. URL http://dx.doi.org/10.1126/science.48.1245.451. [PubMed]

84. STUART-HARRIS CH. Chemotherapy of non-tuberculous pulmonary infections. Br Med J. 1959 Jun;1 (5138):1606–1610. [PMC free article] [PubMed]

85. Stuart-Harris CH. Maxwell finland lecture: the influenza viruses and the human respiratory tract. Rev Infect Dis. 1979;1 (4):592–599. [PubMed]

86. Treanor JJ, Hayden FG, Vrooman PS, Barbarash R, Bettis R, Riff D, Singh S, Kinnersley N, Ward P, Mills RG. Efficacy and safety of the oral neuraminidase inhibitor oseltamivir in treating acute influenza: a randomized controlled trial. US Oral Neuraminidase Study Group. JAMA. 2000 Feb;283 (8):1016–1024. [PubMed]

87. Viboud C, Tam T, Fleming D, Handel A, Miller MA, Simonsen L. Transmissibility and mortality impact of epidemic and pandemic influenza, with emphasis on the unusually deadly 1951 epidemic. Vaccine. Nov, 2006. pp. 6701–6707. URL http://dx.doi.org/10.1016/j.vaccine.2006.05.067. [PubMed]

88. Wearing HJ, Rohani P, Keeling MJ. Appropriate models for the management of infectious diseases. PLoS Med. Jul, 2005. p. e174. URL http://dx.doi.org/10.1371/journal.pmed.0020174. [PMC free article] [PubMed]

89. Whitley RJ, Hayden FG, Reisinger KS, Young N, Dutkowski R, Ipe D, Mills RG, Ward P. Oral oseltamivir treatment of influenza in children. Pediatr Infect Dis J. 2001 Feb;20 (2):127–133. [PubMed]

90. Woodhead MA, Macfarlane JT, McCracken JS, Rose DH, Finch RG. Prospective study of the aetiology and outcome of pneumonia in the community. Lancet. 1987 Mar;1 (8534):671–674. [PubMed]

91. Wu JT, Leung GM, Lipsitch M, Cooper BS, Riley S. Hedging against antiviral resistance during the next influenza pandemic using small stockpiles of an alternative chemotherapy. PLoS Med. May, 2009. p. e1000085. URL http://dx.doi.org/10.1371/journal.pmed.1000085. [PMC free article] [PubMed]

92. Yang Y, Longini IM, Halloran ME. Design and evaluation of prophylactic interventions using infectious disease incidence data from close contact groups. Journal Of The Royal Statistical Society Series C-Applied Statistics. 2006;55:317–330. [PMC free article] [PubMed]

93. Yang Y, Sugimoto JD, Halloran ME, Basta NE, Chao DL, Matrajt L, Potter G, Kenah E, Longini IM. The transmissibility and control of pandemic influenza a (h1n1) virus. Science. Sep, 2009. URL http://dx.doi.org/10.1126/science.1177373. [PMC free article] [PubMed]

94. Yen H-L, Herlocher LM, Hoffmann E, Matrosovich MN, Monto AS, Webster RG, Govorkova EA. Neuraminidase inhibitor-resistant influenza viruses may differ substantially in fitness and transmissibility. Antimicrob Agents Chemother. Oct, 2005. pp. 4075–4084. URL http://dx.doi.org/10.1128/AAC.49.10.4075-4084.2005. [PMC free article] [PubMed]

95. Young LS, LaForce FM, Head JJ, Feeley JC, Bennett JV. A simultaneous outbreak of meningococcal and influenza infections. N Engl J Med. 1972 Jul;287 (1):5–9. [PubMed]

96. Zhanel GG, Ennis K, Vercaigne L, Walkty A, Gin AS, Embil J, Smith H, Hoban DJ. A critical review of the fluoroquinolones: focus on respiratory infections. Drugs. 2002;62 (1):13–59. [PubMed]

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